DimVQ: Unveiling And Addressing Dimensional Collapse In Vector Quantization Models Via Codebook Regularization

Official pre-trained checkpoints for the ICML 2026 paper.

Model Description

DimVQ identifies dimensional collapse in vector quantization models and proposes a simple codebook regularization to restore suppressed low-variance components. This regularization bridges the spectral gap between discrete codebook spaces and continuous representations.

Available Checkpoints

File	Model	Resolution	Codebook Size (K)	Embedding Dim (D)
`simvq_K65536/65536.ckpt`	SimVQ + Codebook Reg.	128x128	65,536	128
`simvq_K65536/65536.yaml`	Config for above	-	-	-
`simvq_K262144/262144.ckpt`	SimVQ + Codebook Reg.	128x128	262,144	128
`simvq_K262144/262144.yaml`	Config for above	-	-	-

Usage

# Load checkpoint
import torch
checkpoint = torch.load("262144.ckpt", map_location="cpu")
model.load_state_dict(checkpoint["state_dict"])

TODO

IBQ checkpoints (K=16384, K=262144, 256x256)
Downstream autoregressive generation models (IBQ-B, IBQ-L, IBQ-XXL)

Citation

@inproceedings{zhang2026dimvq,
  title={Unveiling And Addressing Dimensional Collapse In Vector Quantization Models Via Codebook Regularization},
  author={Zhang, Fang and Zhu, Yongxin and Liu, Yihao and Fu, Bin and Xu, Linli},
  booktitle={International Conference on Machine Learning (ICML)},
  year={2026}
}

jackD
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DimVQ